Prerequisites

1 Course Prerequisites

We assume you are familiar with transport datasets and have basic data analysis skills.

You must have a GitHub account and have saved your username.

We will cover version control concepts in the course.

2 Software Prerequisites

You should bring a laptop with either of the following:

  • Option 1: if you’re using a cloud-based development environment:
    • A modern web browser (e.g., Chrome, Firefox, Edge).
    • A GitHub account (sign up at github.com) and have your username ready.
    • Tested out GitHub Codespaces to ensure it works on your machine by opening this link and running the prerequisites code in prerequisites.qmd:

Open in GitHub Codespaces

or, for running the code locally:

  • Option 2: A laptop with a the necessary software installed, including:
    • An IDE such as VS Code (recommended) or RStudio.
    • R or Python installed (see below for testing code).
    • The gh command-line tool (see cli.github.com for installation and set-up instructions).

4 Testing your setup

You can test your setup by running the following code in Python or R, either in your local IDE or in GitHub Codespaces. Do so by first creating a new Quarto (.qmd) file, e.g. called test.qmd, and then typing the following into a code chunk with three backticks (located in the top left of your keyboard, just left of the 1 key) to start and end the code chunk and {r} or {python} at the end of the first code chunk to make the code interactive, as follows (update the code as needed):

You should be able to run the code in a GitHub Codespaces with the following link:

Open in GitHub Codespaces

Choose either the Python or R code below, depending on which language you prefer to use.

options(repos = c(CRAN = "https://cloud.r-project.org"))
if (!require("pak")) install.packages("pak")
pkgs = c(
    "sf",
    "tidyverse",
    "tmap",
    "osmextract"
)
pak::pak(pkgs)
library(tidyverse)
library(sf)

The next bit optionally downloads lots of data, so you may want to skip it if you’re just testing your setup.

Code 
# Centered on Broadway House, London
broadway_house = stplanr::geo_code("Tothill St, London")
# [1] -0.1302077 51.4996820
study_area = st_point(c(-0.13020, 51.4997)) |> 
  st_sfc(crs = 4326) |>
  st_transform(27700) |>
  st_buffer(500) |>
  st_transform(4326)
extra_tags = c(
  "maxspeed",
  "lit",
  "cycleway"
)
osm_network = osmextract::oe_get_network(
  place = study_area,
  boundary = study_area,
  boundary_type = "clipsrc",
  extra_tags = extra_tags,
  mode = "driving"
)
# sf::write_sf(osm_network, "osm_network.geojson", delete_dsn = TRUE)
osm_network = sf::read_sf("osm_network.geojson")
library(tmap)
tmap_mode("view")
m = tm_shape(osm_network) +
  tm_lines("maxspeed")
m
import osmnx as ox
import geopandas as gpd
import matplotlib.pyplot as plt
import shapely

# Import and plot the saved data:
gdf = gpd.read_file("osm_network.geojson")
gdf.explore(column="maxspeed")

# Optional: Download data from OSM:
study_point = shapely.Point(-0.13020, 51.4997)
study_geom = gpd.GeoSeries([study_point], crs=4326)
study_polygon = study_geom.to_crs(epsg=3857).buffer(500).to_crs(epsg=4326).unary_union
study_polygon_gpd = gpd.GeoDataFrame(geometry=[study_polygon], crs="EPSG:4326")
tags = {"highway": True, "maxspeed": True, "lit": True, "cycleway": True}
gdf = ox.features_from_polygon(study_polygon, tags)
gdf = gdf[gdf.geom_type.isin(["LineString", "MultiLineString"])]
gdf = gdf.to_crs(epsg=3857)
gdf.plot(column="maxspeed", figsize=(10, 10), legend=True)
plt.show()

If it worked, it should look something like this (from the online development version):

That is the road network surrounding Broadway House in London, where the course will be held in person.

Let us know how you get on and if you have any issues getting set up, either by email, or (preferably) via the Discussion forum on GitHub associated with this course repository.

5 Next Steps

Everyone should complete the Pre-Course Questionnaire before the course begins.

Reuse